#这个是在读论文“Deep Residual Shrinkage Networks for Fault Diagnosis”时下载的代码，可以跑通
#有需要的可以学习一下
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 23 21:23:09 2019
Implemented using TensorFlow 1.0 and TFLearn 0.3.2
 
M. Zhao, S. Zhong, X. Fu, B. Tang, M. Pecht, Deep Residual Shrinkage Networks for Fault Diagnosis, 
IEEE Transactions on Industrial Informatics, 2019, DOI: 10.1109/TII.2019.2943898
 
@author: super_9527
"""
  
from __future__ import division, print_function, absolute_import
  
import tflearn
import numpy as np
import tensorflow as tf
from tflearn.layers.conv import conv_2d
  
# 数据加载
from tflearn.datasets import cifar10
(X, Y), (testX, testY) = cifar10.load_data()
  
# 添加噪声
X = X + np.random.random((50000, 32, 32, 3))*0.1
testX = testX + np.random.random((10000, 32, 32, 3))*0.1
  
# 将标签转为 one-hot 格式
Y = tflearn.data_utils.to_categorical(Y,10)
testY = tflearn.data_utils.to_categorical(testY,10)
  
def residual_shrinkage_block(incoming, nb_blocks, out_channels, downsample=False,
                   downsample_strides=2, activation='relu', batch_norm=True,
                   bias=True, weights_init='variance_scaling',
                   bias_init='zeros', regularizer='L2', weight_decay=0.0001,
                   trainable=True, restore=True, reuse=False, scope=None,
                   name="ResidualBlock"):
      
    # residual shrinkage blocks with channel-wise thresholds
  
    residual = incoming
    in_channels = incoming.get_shape().as_list()[-1]
  
    # Variable Scope fix for older TF
    try:
        vscope = tf.variable_op_scope(scope, default_name=name, values=[incoming],
                                   reuse=reuse)
    except Exception:
        vscope = tf.variable_op_scope([incoming], scope, name, reuse=reuse)
  
    with vscope as scope:
        name = scope.name #TODO
  
        for i in range(nb_blocks):
  
            identity = residual
  
            if not downsample:
                downsample_strides = 1
  
            if batch_norm:
                residual = tflearn.batch_normalization(residual)
            residual = tflearn.activation(residual, activation)
            residual = conv_2d(residual, out_channels, 3,
                             downsample_strides, 'same', 'linear',
                             bias, weights_init, bias_init,
                             regularizer, weight_decay, trainable,
                             restore)
  
            if batch_norm:
                residual = tflearn.batch_normalization(residual)
            residual = tflearn.activation(residual, activation)
            residual = conv_2d(residual, out_channels, 3, 1, 'same',
                             'linear', bias, weights_init,
                             bias_init, regularizer, weight_decay,
                             trainable, restore)
              
            # 获取阈值并使用阈值化处理
            abs_mean = tf.reduce_mean(tf.reduce_mean(tf.abs(residual),axis=2,keep_dims=True),axis=1,keep_dims=True)
            scales = tflearn.fully_connected(abs_mean, out_channels//4, activation='linear',regularizer='L2',weight_decay=0.0001,weights_init='variance_scaling')
            scales = tflearn.batch_normalization(scales)
            scales = tflearn.activation(scales, 'relu')
            scales = tflearn.fully_connected(scales, out_channels, activation='linear',regularizer='L2',weight_decay=0.0001,weights_init='variance_scaling')
            scales = tf.expand_dims(tf.expand_dims(scales,axis=1),axis=1)
            thres = tf.multiply(abs_mean,tflearn.activations.sigmoid(scales))
            # soft thresholding
            residual = tf.multiply(tf.sign(residual), tf.maximum(tf.abs(residual)-thres,0))
              
  
            # 下采样
            if downsample_strides > 1:
                identity = tflearn.avg_pool_2d(identity, 1,
                                               downsample_strides)
  
            # 映射到新的维度
            if in_channels != out_channels:
                if (out_channels - in_channels) % 2 == 0:
                    ch = (out_channels - in_channels)//2
                    identity = tf.pad(identity,
                                      [[0, 0], [0, 0], [0, 0], [ch, ch]])
                else:
                    ch = (out_channels - in_channels)//2
                    identity = tf.pad(identity,
                                      [[0, 0], [0, 0], [0, 0], [ch, ch+1]])
                in_channels = out_channels
  
            residual = residual + identity
  
    return residual
  
  
# 实时数据处理
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
  
# 实时数据增强
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([32, 32], padding=4)
  
# Build a Deep Residual Shrinkage Network with 3 blocks
net = tflearn.input_data(shape=[None, 32, 32, 3],
                         data_preprocessing=img_prep,
                         data_augmentation=img_aug)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = residual_shrinkage_block(net, 1, 16)
net = residual_shrinkage_block(net, 1, 32, downsample=True)
net = residual_shrinkage_block(net, 1, 32, downsample=True)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 10, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=20000, staircase=True)
net = tflearn.regression(net, optimizer=mom, loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net, checkpoint_path='model_cifar10',
                    max_checkpoints=10, tensorboard_verbose=0,
                    clip_gradients=0.)
  
model.fit(X, Y, n_epoch=100, snapshot_epoch=False, snapshot_step=500,
          show_metric=True, batch_size=100, shuffle=True, run_id='model_cifar10')
  
training_acc = model.evaluate(X, Y)[0]
validation_acc = model.evaluate(testX, testY)[0]